Data Labeling
Data labeling is the process of tagging data to train machine learning models. We review a range of data labeling tools and services, from audio annotation to image classification, to support AI development.
Image Classification: Applications & Best Practices in 2026
Around 1.72 trillion 1 photos are taken every year. Many are used to train digital solutions, such as self-driving systems powered by image recognition and computer vision (CV) technologies.
Data Labeling for NLP with Real-life Examples in 2026
NLP technology is increasingly being used to enable smart communication between people and their devices. Companies like Google, Amazon, and OpenAI have invested billions in NLP technologies that can understand, interpret, and generate human language with remarkable accuracy. However, behind every sophisticated NLP model lies an important foundation: labeled training data.
Intent Classification in 2026: What it is & How it Works
Customers have more options than ever due to increasing competition and the quality of customer service has become one of the key factors for businesses to stay ahead of the competition. This article, we will explore one of the techniques used in these applications: intent classification.
10 Open Source Data Labeling Platforms in 2026
Data labeling, the process of annotating raw data (such as images, text, or audio), is essential for training ML models to perform tasks like classification and recognition. While pre-built solutions exist, they may not always meet specific needs, making open-source platforms a more flexible and customizable alternative. See the top 10 open-source data labeling tools.
Human Annotated Data in 2026
As the AI market grows (Figure 1), integrating AI solutions remains challenging due to time-consuming tasks like data collection and annotation. Many use automated annotation tools to streamline the tedious process of data annotation, but robust machine learning models still require human-in-the-loop approaches and human-annotated data.